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import matplotlib.pyplot as plt
import numpy as np

# Create a simple approximation of South Korea's map
# The coordinates are normalized and simplified
x = np.array([0, 1, 3, 4, 4, 3, 5, 5, 4, 2, 0, 0])
y = np.array([0, 1, 1, 3, 4, 5, 5, 6, 7, 7, 5, 0])

# Create the plot
plt.figure(figsize=(8, 12))
plt.fill(x, y, 'b-', alpha=0.6, label="대한민국")
plt.title("대한민국 지도 (간략화)")
plt.xlabel("X 좌표")
plt.ylabel("Y 좌표")
plt.grid(True)
plt.legend()
plt.axis('equal')
plt.show()

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import zipfile
import os

# Define the path to the uploaded ZIP file and the extraction directory
zip_file_path = '/mnt/data/gadm36_KOR_shp.zip'
extract_dir = '/mnt/data/gadm36_KOR_shp/'

# Create the extraction directory if it doesn't exist
if not os.path.exists(extract_dir):
    os.makedirs(extract_dir)

# Unzip the file
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

# List the contents of the extraction directory
extracted_files = os.listdir(extract_dir)
extracted_files

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import geopandas as gpd

# Load the shapefile for level 2
shapefile_path = os.path.join(extract_dir, 'gadm36_KOR_2.shp')
gdf = gpd.read_file(shapefile_path)

# Plot the shapefile
plt.figure(figsize=(10, 15))
gdf.boundary.plot(color='blue')
plt.title('South Korea Map (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

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# Plot the shapefile with a thinner boundary line
plt.figure(figsize=(10, 15))
gdf.boundary.plot(color='blue', linewidth=0.5)
plt.title('South Korea Map (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

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# Plot the shapefile with a much thinner boundary line (1/10 of the original thickness)
plt.figure(figsize=(10, 15))
gdf.boundary.plot(color='blue', linewidth=0.05)
plt.title('South Korea Map (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

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# Define the path to the newly uploaded ZIP file and the extraction directory
new_zip_file_path = '/mnt/data/국토교통부_전국 버스정류장 위치정보_20221012.zip'
new_extract_dir = '/mnt/data/bus_station_info/'

# Create the extraction directory if it doesn't exist
if not os.path.exists(new_extract_dir):
    os.makedirs(new_extract_dir)

# Unzip the file
with zipfile.ZipFile(new_zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(new_extract_dir)

# List the contents of the extraction directory
new_extracted_files = os.listdir(new_extract_dir)
new_extracted_files

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import pandas as pd

# Attempt to load the XLSX file
xlsx_file_path = os.path.join(new_extract_dir, new_extracted_files[1])

# Read the XLSX file into a Pandas DataFrame
try:
    bus_station_df = pd.read_excel(xlsx_file_path)
except Exception as e:
    error_message = str(e)

# Show the first few rows of the DataFrame or the error message if reading fails
bus_station_df.head() if 'bus_station_df' in locals() else error_message

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# Skip the initial rows that contain metadata and schema information and read the actual data
try:
    bus_station_df = pd.read_excel(xlsx_file_path, skiprows=5)
    preview_data = bus_station_df.head()
except Exception as e:
    error_message = str(e)

# Show the first few rows of the DataFrame or the error message if reading fails
preview_data if 'preview_data' in locals() else error_message

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# Attempt to load the CSV file
csv_file_path = os.path.join(new_extract_dir, new_extracted_files[0])

# Read the CSV file into a Pandas DataFrame (preview the first few rows)
try:
    bus_station_csv_df = pd.read_csv(csv_file_path, encoding='cp949')  # Encoding might be specific to the file
    csv_preview_data = bus_station_csv_df.head()
except Exception as e:
    csv_error_message = str(e)

# Show the first few rows of the DataFrame or the error message if reading fails
csv_preview_data if 'csv_preview_data' in locals() else csv_error_message

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# Try reading the CSV file with a different encoding
try:
    bus_station_csv_df = pd.read_csv(csv_file_path, encoding='utf-8')  # Try UTF-8 encoding
    csv_preview_data = bus_station_csv_df.head()
except Exception as e:
    csv_error_message = str(e)

# Show the first few rows of the DataFrame or the error message if reading fails
csv_preview_data if 'csv_preview_data' in locals() else csv_error_message

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# Filter the data to include only rows with valid latitude and longitude information
filtered_bus_station_df = bus_station_csv_df.dropna(subset=['위도', '경도'])

# Show the number of rows remaining after filtering and preview the first few rows
remaining_rows = len(filtered_bus_station_df)
preview_filtered_data = filtered_bus_station_df.head()

remaining_rows, preview_filtered_data

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# Plot the South Korea map with level 2 boundaries and bus station locations
plt.figure(figsize=(10, 15))

# Plot the country boundaries with a thin line
gdf.boundary.plot(color='blue', linewidth=0.05, ax=plt.gca())

# Plot the bus station locations as points
plt.scatter(filtered_bus_station_df['경도'], filtered_bus_station_df['위도'], c='black', alpha=0.1, s=0.05)

# Set title and labels
plt.title('South Korea Map with Bus Stations (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

plt.show()

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# Plot the South Korea map with level 2 boundaries and bus station locations in a larger size
plt.figure(figsize=(20, 30))

# Plot the country boundaries with a thin line
gdf.boundary.plot(color='blue', linewidth=0.05, ax=plt.gca())

# Plot the bus station locations as points
plt.scatter(filtered_bus_station_df['경도'], filtered_bus_station_df['위도'], c='black', alpha=0.1, s=0.05)

# Set title and labels
plt.title('South Korea Map with Bus Stations (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

plt.show()

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# Increase the size of the points for better visibility
plt.figure(figsize=(20, 30))

# Plot the country boundaries with a thin line
gdf.boundary.plot(color='blue', linewidth=0.05, ax=plt.gca())

# Plot the bus station locations as points with increased size
plt.scatter(filtered_bus_station_df['경도'], filtered_bus_station_df['위도'], c='black', alpha=0.1, s=10)

# Set title and labels
plt.title('South Korea Map with Bus Stations (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

plt.show()

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# Filter the geographical extent based on the given latitude and longitude constraints
filtered_bus_station_df = filtered_bus_station_df[(filtered_bus_station_df['위도'] > 32.5) & (filtered_bus_station_df['경도'] > 120)]

# Plot the map and bus station locations with the specified geographical extent
plt.figure(figsize=(20, 30))

# Plot the country boundaries within the specified geographical extent
gdf_filtered = gdf.cx[120:, 32.5:]
gdf_filtered.boundary.plot(color='blue', linewidth=0.05, ax=plt.gca())

# Plot the bus station locations as points within the specified geographical extent
plt.scatter(filtered_bus_station_df['경도'], filtered_bus_station_df['위도'], c='black', alpha=0.1, s=10)

# Set title and labels
plt.title('Filtered South Korea Map with Bus Stations (Level 2)')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

plt.show()